Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
A course in fuzzy systems and control
A course in fuzzy systems and control
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
A New Approach to Fuzzy Classifier Systems
Proceedings of the 5th International Conference on Genetic Algorithms
For real! XCS with continuous-valued inputs
Evolutionary Computation
Be real! XCS with continuous-valued inputs
GECCO '05 Proceedings of the 7th annual workshop on Genetic and evolutionary computation
XCSF with computed continuous action
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Classifier fitness based on accuracy
Evolutionary Computation
Three architectures for continuous action
IWLCS'03-05 Proceedings of the 2003-2005 international conference on Learning classifier systems
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uQFCS is a generalization of QFCS presented previously in which the condition of fixed fuzzy sets imposed to QFCS is eliminated. Therefore, these fuzzy sets are evolved with the action parts of the fuzzy rules. uQFCS also can solve the multi-step reinforcement learning problem in continuous environments and with a set of continuous vector actions. This paper presents results that show that uQFCS can also evolve rules to represent only those parts of the input and action space where the expected values are important for making decisions. Results for the uQFCS are compared with those obtained by Q-learning and QFCS. uQFCS has similar performance to QFCS. uQFCS was tested in the Frog Problem and in five versions of the n-Environment Problem from which two of them are problems of one inertial particle.